Predicting life expectancy#
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It is no secret that life expectancy has been increasing rapidly over the past couple of decades. A crucial indicator of a nation’s health and well-being can be traced back to its life expectancy statistic. This statistic is influenced by a multitude of factors, such as: economic stability, healthcare quality lifestyle, education, environmental conditions and many more. The question begs however, which of these factors contribute the most to a nation’s life expectancy? One might argue that only education plays a role, because all other factors are dependent on it. Another person might argue that not all of these factors are dependent on a nation’s level of education, thus its impact might not be as significant as one expects. This project aims to put these two perspectives to the test, by analyzing several key factors contributing to a nation’s life expectancy.
Several datasets about factors related to life expectancy are used in this project. Using sophisticated modeling techniques and visualization, relevant data of these factors are compared to eachother. The objective is to provide both perspectives with sufficient arguments to defend their statement. The insights provided in this project may help determine whether education is the only factor contributing to life expectancy.
It is important to note the pace at which life expectancy has skyrocketed over the past decades. The extraordinary rise is attributed to a wide range of advances in human development. At the start of the nineteenth century, no region had a life expectancy higher than 40 years. Nowadays, multiple countries are close to hitting 80 years, according to ourworldindata. This rapid increase in life expectancy can be visualized using a box plot.
According to this graph, life expectancy only really started rising quickly after World War II. A prime example being Japan, where life expectancy increased a staggering 13.5 years in just one decade (Sugiura et al., 2010).
This observation rises the question what factors contributed to such a fast rise. In the next two chapters, two perspectives are discussed on this matter. The first chapter argues that a nation’s life expectancy merely depends on its education level and GDP. The second chapter refutes this perspective, explaining why the expectancy comes from a multitude of factors, that can also be independent of GDP and education.
The impact of Education and GDP on Life Expectancy#
The first perspective holds that education and Gross Domestic Product (GDP) are the primary factors influencing a country’s life expectancy. This view is built upon the idea that higher levels of education lead to better health awareness and healthcare access. Supplementary to this, a strong GDP ensures that resources required for public healthcare are made available. According to this perspective, these two factors alone are sufficient enough to explain the variations in life expectancy across all countries. Mapping the data of education level and life expectancy together into a world map, a positive correlation can be found.
Reviewing the map, it is evident that most countries exhibit high life expectancy, most particularly in western nations. One possible explanation for this correlation could be that people with better education tend to adopt healthier lifestyles. The education variable represents primary and lower secondary schooling, where children get taught how to use critical thinking for daily tasks such as observing and asking questions. This enhanced ability to make informed decisions likely contributes to increased life expectancy, as people with better education make healthier choices. (Raghupathi & Raghupathi, 2020b)
Education level#
If assumed that education is one of the two core factors determining life expectancy, one may conclude that only primary and (lower) secondary education can be used to predict life expectancy. However, the world map did not contain the completion rate of tertiary education, which includes college and university. Tertiary education allows for a population to specialize in certain fields, including healthcare.
Analyzing the data on completion rate between the different levels, we find a noticeable increase in secondary and tertiary completion rates:
The graph shows an increasing trend in life expectancy when secondary and tertiary education have higher completion rates. It is essential to note that the four countries chosen to illustrate this trend do not represent every country in their category. Generally speaking however, an increase can almost always be found.
Education and GDP#
One can also argue that a society with adequate education will produce an increasing GDP. Research at the university of Munich has shown that people with higher quality education are able to achieve jobs with more complex skill sets, resulting in a higher paying job. When more people obtain these higher paying jobs, the GDP of the country of origin will increase. This in turn will influence the life expectancy of the country. Research originating from the University of Zagreb has shown that an increase in GDP of a country also has a positive influence on the country’s life expectancy. This claim can be visualized using the data of GDP and education completion rate. For the following graph, secondary education completion rate was used to compare GDP. The graph shows that an increase in education correlates with an increase in GDP, which in turn delivers an increase in life expectancy.
The trendline in the graph above signals a positive correlation between secondary education completion rate and GDP per capita. Using this information we can confirm the claim that higher completion rate contributes to the population acquiring higher paying jobs, thus contributing to the GDP per capita. Note that the outliers in the graph may cause the trend line to look less steep than it really is. The increase from 40% to 80% is around $15000, a life changing amount for most people.
Life expectancy cannot be predicted by just education#
Even though A country investing in their education program results in an increase in life expectancy. There are more direct approaches to increasing a country’s life expectancy. One possible solution is investing in increasing the country’s vaccination rate. Diseases or viruses like Polio and Diphtheria can be fatal if not treated appropriately, in some cases (like for polio) there is no cure at all. Not treating these diseases results in a drastic decrease in life expectancy. So instead of investing in education to improve life expectancy, a country should invest in vaccines as this has a more direct effect. This can be seen in the plot where it shows an increase in vaccination rate for polio and Diphtheria corresponds with an increase in life expectancy. This is also found in the research by Jenifer Ehreth. Which concludes that improving the vaccination rate is a big factor in increasing a country’s life expectancy. https://www.sciencedirect.com/science/article/pii/S0264410X03003773
Unhealthy lifestyles#
The prevelance of unhealthy lifestyles in (developed) countries may also contribute to life expectancy.
Counter argument 2#
Another way to increase life expectancy is to invest in cleaner and safer drinking water. Unsafe drinking water is the cause of a lot of different diseases, all of which can cause a person to live a shorter life. It can be seen in the graph that an increase in the amount of people that drink from a safe water source correlates with an increase in life expectancy, this also supported by the following research paper, Angelakis et al. (2021b). This means that it should be useful for a country to invest in a clean water source before it starts to invest in different areas.
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A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/tmp/ipykernel_249501/1790060808.py:15: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
The impact of vaccination#
Another factor to consider is
Conclusion#
To conclude this research, if a country wants to increase their life expectancy, they shouldn’t focus all of their investment into just one of the factors that were discussed. All of the factors that came forward can influence a country’s life expectancy in a positive way. So the wisest thing to do would be to spread the investment over different factors, to ensure a decrease in the occurrence of all the different causes of a reduced life expectancy making sure that different causes of life loss might be reduced in occurrence, or even completely removed (like cases of polio or other preventable diseases).
References#
Raghupathi, V., & Raghupathi, W. (2020). The influence of education on health: an empirical assessment of OECD countries for the period 1995–2015. Archives Of Public Health, 78(1). https://doi.org/10.1186/s13690-020-00402-5
Ehreth, J. (2003). The value of vaccination: a global perspective. Vaccine, 21(27–30), 4105–4117. https://doi.org/10.1016/s0264-410x(03)00377-3
Angelakis, A. N., Vuorinen, H. S., Nikolaidis, C., Juuti, P. S., Katko, T. S., Juuti, R. P., Zhang, J., & Samonis, G. (2021). Water Quality and Life Expectancy: Parallel Courses in Time. Water, 13(6), 752. https://doi.org/10.3390/w13060752
Sugiura, Y., Ju, Y. S., Yasuoka, J., & Jimba, M. (2010). Rapid increase in Japanese life expectancy after World War II. Biosci Trends, 4(1), 9-16.